Predictors of underage pregnancy among women aged 15–19 in highly prevalent regions of Ethiopia: a multilevel analysis based on EDHS, 2016

Under age (teenage) pregnancy is a pregnancy that occurs under the age of 20 years old. Its magnitude is increasing globally. It is much higher in low-income countries compared to high-income countries. Teenage pregnancy exposed teenagers to various obstetric and perinatal complications. However, its predictors are not well investigated in highly prevalent regions of Ethiopia. Therefore, this study assessed individual and community-level predictors of teenage pregnancy using a multi-level logistic regression model. An in-depth secondary data analysis was performed using the fourth Ethiopian Demographic and Health Survey (EDHS) 2016 data set. A weighted sample of 2397 teenagers was included in the final analysis. Multi co linearity and chi-square tests were checked and variables which did not fulfill the assumptions were excluded from the analysis. Four models were fitted. Variables with p value ≤ 0.2 in the bi-variable multilevel logistic regression were included in the multivariable multilevel logistic regression. The adjusted odds ratio (AOR) with a 95% confidence interval (95% CI) was computed. Variables with a p value of less than 0.05 in the multi-variable multilevel logistic regression were declared as statistically significant predictors. A total of 2397 weighted participants aged from 15 to 19 were involved. About 15% of teenagers were pregnant. Age [17 (AOR = 9.41: 95% CI 4.62, 19.13), 18 (AOR = 11.7: 95% CI 5.96, 23.16), 19 (AOR = 24.75: 95% CI 11.82, 51.82)], primary education (AOR = 2.09: 95% CI 1.16, 3.76), being illiterate (AOR = 1.80: 95% CI 1.19, 2.73), religion [being Muslims (AOR: 2.98:95% CI 1.80, 4.94), being Protestants (AOR = 2.02: 95% CI 1.20, 3.41)], contraceptive non use (AOR = 0.18: 95% CI 0.11, 0.31), a high proportion of family planning demand (AOR = 3.52: 95% CI 1.91, 6.49), and a high proportion of marriage (AOR = 4.30: 95% CI 2.25, 8.21) were predictors of teenage pregnancy. Age, educational status, religion, contraceptive non-use, literacy proportion of marriage and proportion of demand for family planning were the most significant predictors of teenage pregnancy. The ministry of education shall focus on universal access to education to improve female education. The government should work in collaboration with religious fathers to address reproductive and sexual issues to decrease early marriage and sexual initiation. Especial attention should be given to teenagers living in a community with a high proportion of marriage.

Study design, period and sampling. An in-depth secondary data analysis was performed using the fourth Ethiopian Demographic and Health survey (EDHS) 2016 data set. The 2016 EDHS was done in nine regional states and two administrative cities using cross-sectional study design. The EDHS was based on 645 enumeration areas. Details of the EDHS methodology are found on the EDHS reports 13 . The EDHS has been conducted every 5 years to provide health and health-related indicators in Ethiopia.
Data source and study population. We  www.nature.com/scientificreports/ enumeration areas (EAs) were included in this study. A total of 2711 younger women aged [15][16][17][18][19] years were interviewed about teenage pregnancy at the time of the survey after weighting, a total of 2397 teenagers were included in the final analysis. All the frequencies and percentages in the result section were weighted.
Variables and measurement. The outcome variable was teenage pregnancy. It was dichotomized as (yes/ no). A woman was considered as experiencing teenage pregnancy if her age was from 15 to 19 and had a birth or was pregnant at the time of the interview. The independent variables were grouped under individual-level variables and community level variables. Individual level variables include age, marital status, educational status, literacy, religion, working status, wealth index, age at marriage, age at first sex, media exposure, contraceptive use, demand to family planning, hearing family planning, messages on mass media, sex of household head and age of household head. Whereas community level variables include residence, community wealth, community literacy, community education, community media exposure, community family planning demand, community percentage of marriage, community family planning message transfer and community working status.
Individual level variables. Age at first marriage. The respondent's age at first marriage is the age at which she began living with her first spouse/partner. It was divided into three groups. "Married before the age of 15", "married between the ages of 15 and 17", and "not married before the age of 18". Those who were not married before the age of 18 include those who were married after the age of 18 and those who were not married during their lifetime.
Sexual experience. Was categorized into four as "never had sex, "active before age 15", "active between ages 15 and 17", and "active at age 18 and above".
Educational status of women. This variable is divided into three categories: "no education", "primary", and "secondary and higher education".
Working status. This has been categorized as "Yes" and "No" in the 2016 EDHS.
Media exposure. Watching television (TV), listening to the radio and reading newspapers both less than once a week and at least once a week were considered to measure exposure to media.
Wealth index. Within the dataset, the wealth index was presented as Poorest, Poorer, Middle, Richer, and Richest. In this study, a new variable was generated with three categories as "Poor", "Middle" and "Rich" by merging poorest with poorer and richest with richer.
Religion. In the 2016 EDHS, religion was categorized as Orthodox, Muslim, Protestant, Catholic, traditional followers and others. In this study, the former three were encoded independently and Catholic and traditional religion followers were merged into the "others" category.
Hearing family planning messages. This variable was generated from the four sources of messages related to family planning "Heard family planning on radio last few months", "Heard family planning on radio last few months", and "Heard family planning on radio last few months". These were measured as "yes or no" in the 2016 EDHS. In this study, participants were considered to hear family planning messages if they said "yes" at least for one of the sources.
Literacy. In the EDHS 2016 this variable was recorded as cannot read at all, Able to read only parts of a sentence, Able to read the whole sentence, having No card with required language and being Blind/visually impaired. In this study, it was coded as literate (those able to read) and illiterate (the rest categories).
Community level variables. Community-level variables were computed by aggregating the individual level women's characteristics into clusters. Then the proportion was calculated by dividing subcategories by the total. Distributions of the proportion of aggregate variables were checked using the Shapiro-Wilk normality test and were not normally distributed. Therefore, these aggregate variables were categorized using the median value. A total of eight community variables were generated. A residence was taken as a community-level variable. Therefore a total of nine community variables were tested (residence, community wealth, community literacy, community education, community family planning demand, community media exposure, community level marriage, community family planning message transfer, and community working status).
Data processing and analysis. Descriptive statistics including frequencies, medians, and percentages were produced once the data had been cleaned. Stata version 14.0 was used to analyze the data. Sampling weights were used to account for the sample's non-proportional strata allocation and non-responses. Individuals were nested inside communities in the EDHS data, and the intra-class correlation coefficient (ICC) was 29.33%. To evaluate the independent (fixed) effects of the explanatory variables as well as the community-level random effects on teenage pregnancy, a two-level mixed-effects logistic regression model was used. Multi-co linearity was checked and variables with a variance inflation factor greater than 10 were excluded. Some variables which did not fulfill the chi-square test were also excluded from the analysis. This study used four models [Model 0 (no factors), Model 1 (individual level factors), Model 2 (only community-level factors), and Model 3 (both individual and community-level factors)]. The multivariable multilevel logistic regression analysis includes variables with a p value of 0.2 from the bi-variable multilevel logistic regression analysis. The Adjusted Odds Ratio (AOR) with a 95% confidence interval (95% CI) was computed. Variables with a p value of less than 0.05 in the multi-variable multilevel logistic regression analysis in the final model were declared as statistically significant predictors of the outcome variable.

Results
In this study, a total of 2397 weighted adolescent girls were participated. The mean (± SD) age of study participants was 16.8 (± 1.36). One thousand eight hundred seven (78.6%) were ever married. The majority of study participants, 1577 (65.8%) had primary education. Only 4.88% (117) of participants were contraceptives users (Table 1).
One thousand nine hundred fifty-five (81.5) of the study participants were rural dwellers. Nearly half (47.4%) of the participants were from communities with a high proportion of poorness. One thousand three hundred (55.59%) participants were from communities with a low proportion of above secondary education. About 47% (1132) of teenagers were from communities with a high proportion of early marriage ( Table 2).
Predictors of teenage pregnancy. Multilevel mixed effect logistic regression model was employed.
The measures of variations or random effects were reported using intra-class correlation (ICC), a proportional change in variance (PCV), and Median Odds Ratio (MOR). PCV was computed as: PCV = Vnull−VA Vnull 37 and MOR is a measure of unexplained cluster heterogeneity and it was computed as: MOR = e 0.95 √ VA37 where "VA" represents the area or cluster level variance. The ICC was used to show how much the observation within one cluster resembled each other and it was generated directly from each model using "estat ICC" command following regression. The model comparison was done using the likelihood ratio. The model with highest likely hood ratio was selected, and (Table 3).
In the multilevel mixed effect multivariable logistic regression model, the age of respondents, education, literacy, religion, contraceptive use, community level demand for family planning, and proportion of marriage in the community were statistically significant predictors of teenage pregnancy.

Discussion
The age of respondents, educational status, literacy, contraceptive use, religion, family planning demand at the community level and proportion of marriage in the community were found to be significant predictors of teenage pregnancy. Regarding age, teenagers aged 17, 18, and 19 had higher odds of experiencing teenage pregnancy than 15 years olds teenagers. This is similar to other studies done in Ethiopia 8,38,39 and Kenya 26 . The possible explanation for this finding could be, as teenagers get older, the probability of being sexually active and getting married will be increased. Consequently, the chance of getting pregnant and childbirth will also increase. This implies that sexual and reproductive health programs (SRH) should be designed to focus on late teenagers.
Level of education was also found to be a predictor of teenage pregnancy. In this study, teenagers with primary education have higher odds of teenage pregnancy compared to teenagers with secondary education and above. This finding is in agreement with the findings of studies from Ethiopia 38,40 , Malawi 27 , Australia 41 , East Africa countries 42 , Kenya 26,43 , and European Union countries 25 which states better education results a fall in teenage pregnancy 44 . The possible explanation could be, teenagers with higher levels of education are accessible to relevant information and would have better knowledge of reproductive and sexual health such as the risk of unprotected sex, consequences of early pregnancy and preventive measures 27 . This implies that teenagers should be educated to at least secondary education.
Surprisingly, contraceptive use increases the odds of teenage pregnancy in our study. Though our study finding is contradicted with many scientific pieces of evidence from Ethiopia 30,38-40 , Malawi 27 , Uganda 45 , it is supported by study finding from Kenya 26 . One study concludes that family planning has an ambiguous impact on teen pregnancy and no evidence that the provision of family planning reduces teenage pregnancy 46 . The possible explanation for this finding could be poor quality and efficacy of birth control methods and services, and improper use of contraceptives can result in unwanted pregnancies. In our study, most of the contraceptive non-users are not in a marital union or are sexually active and have no risk of pregnancy. www.nature.com/scientificreports/ possible explanation can be due to the likely hood of Muslims marrying at an age less than 15 years 47,50 . Concerning being protestant, there was supportive evidence in Ghana which shows the liberal attitude of women towards sexual activity increases the likely hood of women's premarital sexual intercourse and underage pregnancy 51 . The difference in teenage pregnancy among different religions may also be explained by the difference in attitudes, norms and beliefs about birth control and the value of children among different religions 52,53 . This implies that there should be collaboration with religious fathers to prevent teenage pregnancy and its complications. Another predictor of teenage pregnancy was the proportion of marriage in the community. This study revealed that teenager from a community with a high proportion of marriage has higher odds of experiencing teenage pregnancy. This finding was supported by findings from Uganda 45,54 and Nigeria 55,56 . The percentage of women in sexual union and frequency of sexual intercourse is the most important proximal determinants of fertility 57 . For pregnancy to occur sexual intercourse is a must and marriage increases the frequency of sexual contact. This implies that the legal age of marriage should be strictly followed to prevent early marriage and to reduce the percentage of marriage in the community.
The proportion of family planning demand in the community was also a predictor of teenage pregnancy. This shows that teenagers in a community with a high proportion of family planning have higher odds of teenage pregnancy. This finding was supported by evidence from Washington 58 . The possible explanation is that, larger proportion of family planning demand in adolescents is unmet need for contraception resulting in unintended pregnancies 58 . This implies that the family planning needs of teenagers should be met through expanded SRH services. Literacy was also found to predict the rate of teenage pregnancy. In this study, illiterate teenagers had higher odds of teenage pregnancy than literate. This finding was supported by evidence from the united state 59 . A possible explanation could be literate teenagers have a better understanding of reproductive and sexual issues through reading printed materials like newspaper magazines and books.  www.nature.com/scientificreports/ Strengths and limitations. We believe our study had several strengths such as we used nationwide data with better statistical power and used multilevel approaches. However, using secondary data limit the researcher to measure all possible determinants like culture and tradition-related factors. The accuracy of the data could be affected by recall bias since the source of the data was self-report.
Ethical approval and consent to participate. Since this study was conducted based on EDHS data which is available by request from the measure DHS website (http:// www. measu redhs. com), ethics approval was not required for this study. All methods of this research were done following the declaration of Helsinki. The data was collected anonymously during the survey and used anonymously during the current analysis.